Evaluation

Precision and Recall

Two complementary accuracy metrics: Precision asks "of the things I flagged, how many were correct?" Recall asks "of all correct things, how many did I find?"

Definition

Precision = true positives / (true positives + false positives). Recall = true positives / (true positives + false negatives). The F1 score is their harmonic mean. Critical for imbalanced datasets.

Why it matters

Choosing between precision and recall depends on the cost of errors, missing fraud (low recall) vs. false alarms (low precision).

Where Sophizo applies this

Sophizo deploys Precision and Recall inside revenue and AI engagements with growth-stage operators and PE-backed portfolios.

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Related terms in Evaluation

Agent Evals
Standardized tests for AI agents to prove they are smart, safe, and reliable before they are deployed.
AI Model Monitoring
Keeping a constant watch on a deployed AI to make sure it hasn't gotten broken or less accurate over time.
Area Under the Curve (AUC)
A score from 0 to 1 that tells you how good your model is at distinguishing between two things (like spam vs. not spam).
Conformal Prediction
A technique that tells you not just what the AI predicts, but how confident it is, with a mathematical guarantee.
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